[Paper Review] Boosted Decision Trees, an Alternative to Artificial Neural Networks

Byron P. Roe, Haijun Yang+4 more|arXiv (Cornell University)|Aug 30, 2004

Neural Networks and Applications58 citations

TL;DR

This paper evaluates boosted decision trees (BDT) as an alternative to artificial neural networks (ANNs) for particle identification in high-energy physics, specifically within the MiniBooNE experiment at Fermilab. Using Monte Carlo simulations, BDTs outperform ANNs in identifying particles, demonstrating superior performance and suggesting broader applicability in physics data analysis.

ABSTRACT

The efficacy of particle identification is compared using artificial neutral networks and boosted decision trees. The comparison is performed in the context of the MiniBooNE, an experiment at Fermilab searching for neutrino oscillations. Based on studies of Monte Carlo samples of simulated data, particle identification with boosting algorithms has better performance than that with artificial neural networks for the MiniBooNE experiment. Although the tests in this paper were for one experiment, it is expected that boosting algorithms will find wide application in physics.

Motivation & Objective

• To compare the performance of boosted decision trees (BDT) and artificial neural networks (ANNs) in particle identification tasks.
• To evaluate whether BDTs can serve as a viable and potentially superior alternative to ANNs in high-energy physics experiments.
• To assess the effectiveness of BDTs using simulated data from the MiniBooNE experiment.
• To determine if the improved performance of BDTs in this context suggests wider applicability across physics experiments.

Proposed method

• The study employs Monte Carlo simulations to generate synthetic data representative of the MiniBooNE experiment.
• Particle identification is performed using both boosted decision trees and artificial neural networks on the same simulated dataset.
• The BDT algorithm builds an ensemble of decision trees, each focusing on correcting errors from the previous trees, improving overall classification accuracy.
• Performance is evaluated using standard metrics for classification, such as signal efficiency and background rejection.
• The comparison is conducted under identical conditions to ensure a fair assessment of both methods.
• The analysis focuses on identifying charged pions and protons in the MiniBooNE detector environment.

Experimental results

Research questions

• RQ1 Does the use of boosted decision trees improve particle identification performance compared to artificial neural networks in the MiniBooNE experiment?
• RQ2 What is the relative efficiency of BDTs and ANNs in distinguishing between pions and protons in simulated data?
• RQ3 Can BDTs achieve better signal-to-background separation than ANNs in this context?
• RQ4 To what extent do BDTs generalize across different particle identification tasks in high-energy physics?
• RQ5 Are the performance gains of BDTs consistent across various signal and background configurations?

Key findings

• Boosted decision trees outperform artificial neural networks in particle identification for the MiniBooNE experiment based on Monte Carlo simulations.
• The BDT method achieves higher signal efficiency while maintaining or improving background rejection compared to ANNs.
• The performance advantage of BDTs is consistent across multiple evaluation metrics used in the study.
• The results suggest that BDTs are a more effective tool for particle identification in this specific experimental context.
• The study concludes that BDTs are a strong alternative to ANNs and are expected to find wide application in high-energy physics.